Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China

Closing the loop between environment, brain and mental health: how far we might go in real-life assessments?

PURPOSE OF REVIEW

Environmental factors such as climate, urbanicity, and exposure to nature are becoming increasingly important influencers of mental health. Incorporating data gathered from real-life contexts holds promise to substantially enhance laboratory experiments by providing a more comprehensive understanding of everyday behaviors in natural environments. We provide an up-to-date review of current technological and methodological developments in mental health assessments, neuroimaging and environmental sensing.

RECENT FINDINGS

Mental health research progressed in recent years towards integrating tools, such as smartphone based mental health assessments or mobile neuroimaging, allowing just-in-time daily assessments. Moreover, they are increasingly enriched by dynamic measurements of the environment, which are already being integrated with mental health assessments. To ensure ecological validity and accuracy it is crucial to capture environmental data with a high spatio-temporal granularity. Simultaneously, as a supplement to experimentally controlled conditions, there is a need for a better understanding of cognition in daily life, particularly regarding our brain's responses in natural settings.

SUMMARY

The presented overview on the developments and feasibility of "real-life" approaches for mental health and brain research and their potential to identify relationships along the mental health-environment-brain axis informs strategies for real-life individual and dynamic assessments.

Associations of street-view greenspace with Parkinson's disease hospitalizations in an open cohort of elderly US Medicare beneficiaries

INTRODUCTION

Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization).

METHODS

We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization.

RESULTS

There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density.

CONCLUSION

Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Quantitative evaluation of urban green exposure and its impact on human health: A case study on the 3-30-300 green space rule

BACKGROUND AND AIMS

Urban green spaces offer various health benefits, yet the impact of comprehensive green exposure criteria on multidimensional health remains unclear. The 3-30-300 green space rule represents the green exposure indicators with specific thresholds. This study aims to quantitatively evaluate urban green exposure in cities and can support investigation of its relationship with human health.

METHODS

We conducted a cross-sectional study based on 902 investigated individuals in 261 residential locations aged 11-95 years from Xiamen City, China. 3-30-300 green exposure was calculated using field surveys, GIS, and Baidu Maps Application Programming Interface (API). Physical health data was based on Occupational Stress Indicator (OSI)-2. Mental health was from the 12-item General Health Questionnaire (GHQ-12). Social health was from a self-constructed evaluation questionnaire. Statistical analyses were conducted using Geographically Weighted Regression and Geographically Weighted Logistic Regression for global and local effects on green exposure and multidimensional health.

RESULT

Among the investigated individuals, only 3.55 % (32/902) fully meet the 3-30-300 rule in Xiamen. Global results show that individuals achieved at least 30 % vegetation coverage (Yes) is associated with better physical (β: 0.76, p < 0.01) and social (β: 0.5, p < 0.01) health. GWLR global results indicate that individuals can "see at least 3 trees from home" meeting one (OR = 0.46, 95%CI: 0.25-0.86, p < 0.05) or two (OR = 0.41, 95%CI: 0.22,0.78, p < 0.01; OR = 0.24, 95%CI: 0.07-0.77, p < 0.05) 3-30-300 rule components are significantly associated with reduced medical visits and hospitalizations refer to not met these criterias. In the GWR local analysis, achieved 30 % vegetation cover is significantly related to improved social health at all locations. Meeting any two indicators also contribute to improved social health (n = 511, β: 0.46-0.51, P < 0.05).

CONCLUSION

Green exposure indicators based on the 3-30-300 rule guiding healthy urban green space development. We observed multidimensional health benefits when 1/3 or 2/3 of the indicators were met.

Assessing the Impact of Urban Environments on Mental Health and Perception Using Deep Learning: A Review and Text Mining Analysis

Understanding how outdoor environments affect mental health outcomes is vital in today's fast-paced and urbanized society. Recently, advancements in data-gathering technologies and deep learning have facilitated the study of the relationship between the outdoor environment and human perception. In a systematic review, we investigate how deep learning techniques can shed light on a better understanding of the influence of outdoor environments on human perceptions and emotions, with an emphasis on mental health outcomes. We have systematically reviewed 40 articles published in SCOPUS and the Web of Science databases which were the published papers between 2016 and 2023. The study presents and utilizes a novel topic modeling method to identify coherent keywords. By extracting the top words of each research topic, and identifying the current topics, we indicate that current studies are classified into three areas. The first topic was "Urban Perception and Environmental Factors" where the studies aimed to evaluate perceptions and mental health outcomes. Within this topic, the studies were divided based on human emotions, mood, stress, and urban features impacts. The second topic was titled "Data Analysis and Urban Imagery in Modeling" which focused on refining deep learning techniques, data collection methods, and participants' variability to understand human perceptions more accurately. The last topic was named "Greenery and visual exposure in urban spaces" which focused on the impact of the amount and the exposure of green features on mental health and perceptions. Upon reviewing the papers, this study provides a guide for subsequent research to enhance the view of using deep learning techniques to understand how urban environments influence mental health. It also provides various suggestions that should be taken into account when planning outdoor spaces.

Measuring Greenspace in Rural Areas for Studies of Birth Outcomes: A Comparison of Street View Data and Satellite Data

Using street view data, in replace of remotely sensed (RS) data, to study the health impact of greenspace has become popular. However, direct comparisons of these two methods of measuring greenspace are still limited, and their findings are inconsistent. On the other hand, almost all studies of greenspace focus on urban areas. The effectiveness of greenspace in rural areas remains to be investigated. In this study, we compared measures of greenspace based on the Google Street View data with those based on RS data by calculating the correlation between the two and evaluating their associations with birth outcomes. Besides the direct measures of greenness, we also compared the measures of environmental diversity, calculated with the two types of data. Our study area consists of the States of New Hampshire and Vermont, USA, which are largely rural. Our results show that the correlations between the two types of greenness measures were weak to moderate, and the greenness at an eye-level view largely reflects the immediate surroundings. Neither the street view data- nor the RS data-based measures identify the influence of greenspace on birth outcomes in our rural study area. Interestingly, the environmental diversity was largely negatively associated with birth outcomes, particularly gestational age. Our study revealed that in rural areas, the effectiveness of greenspace and environmental diversity may be considerably different from that in urban areas.

Unlocking ground-based imagery for habitat mapping

Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.

Proximity to public green spaces and depressive symptoms among South African residents: a population-based study

BACKGROUND

Exposure to green spaces has been suggested to improve mental health and may reduce the risk of depression. However, there is generally limited evidence on the association between green spaces and depression originating from low-and middle-income countries and Africa in particular. Here, we investigate the association between proximity to public green spaces and depressive symptoms among residents of Gauteng Province, South Africa.

METHODS

We used data from the 2017/2018 Gauteng quality of life survey. We included all individuals aged 18 years or older residing in the nine municipalities of Gauteng Province that completed the survey (n = 24,341). Depressive symptoms were assessed using the Patient Health Questionnaire-2. Proximity to public green spaces was defined as self-reported walking time (either less or greater than 15 min) from individuals' homes to the nearest public green space. To assess the association between access to public green spaces and depressive symptoms, we used mixed-effects models, adjusted for age, sex, population group (African, Indian/Asian, Coloured (mixed race), and White), educational attainment, and municipality. We additionally performed stratified analyses by age, sex, educational attainment, and population group to evaluate whether associations differed within subgroups. Associations are expressed as prevalence ratios (PR) and their 95% confidence intervals (95% CI).

RESULTS

We observed a 6% (PR = 0.94, 95%CI = 0.92-0.96) prevalence reduction in depressive symptoms for individuals who reported that the nearest public green space was less than 15 min from their homes as compared to those who reported > 15 min. After stratification, this inverse association was stronger among females, individuals aged 35-59 years,those with higher levels of educational attainment, and Coloured individuals as compared to their counterparts.

CONCLUSION

Our findings suggest that public green spaces close to residential homes may be associated with a reduction in the occurrence of depressive symptoms among urban populations in resource-constrained settings like South Africa.

Protocol for assessing neighborhood physical disorder using the YOLOv8 deep learning model

Neighborhood physical disorder (PD), characterized by disruptions and irregularities in spatial elements, is associated with negative economic, social, and public health outcomes. Here, we present a protocol to quantitatively assess PD utilizing a range of metrics. We describe steps for collecting street views, constructing detection models using the YOLOv8 deep learning model, calculating PD scores, and quantifying changes in PD across streets and cites. This protocol serves as a methodological foundation for assessing PD in different countries and regions. For complete details on the use and execution of this protocol, please refer to Chen et al.1.

Current methodologies of greenspace exposure and mental health research-a scoping review

Introduction

Greenspaces can provide an important resource for human mental health. A growing body of literature investigates the interaction and the influence of diverse greenspace exposures. In order to gain a comprehensive understanding of the complex connection between greenspace and mental health, a variety of perspectives and methodological combinations are needed. The aim of this review is to assess the current methodologies researching greenspace and mental health.

Methods

A scoping review was conducted. Four electronic databases (Pubmed, Embase, PsycInfo, Web of Science) were searched for relevant studies. A wide range of greenspace and mental health keywords were included to provide a comprehensive representation of the body of research. Relevant information on publication characteristics, types of greenspaces, mental health outcomes, and measurements of greenspace exposure and mental health was extracted and assessed.

Results

338 studies were included. The included studies encompassed a multitude of methods, as well as outcomes for both greenspace and mental health. 28 combinations were found between seven categories each for greenspace and mental health assessment. Some pairings such as geoinformation systems for greenspace assessment and questionnaires investigating mental health were used much more frequently than others, implying possible research gaps. Furthermore, we identified problems and inconsistences in reporting of greenspace types and mental health outcomes.

Discussion

The identified methodological variety is a potential for researching the complex connections between greenspace and mental health. Commonly used combinations can provide important insights. However, future research needs to emphasize other perspectives in order to understand how to create living environments with mental health benefits. For this purpose, interdisciplinary research is necessary.

A systematic review of audit tools for evaluating the quality of green spaces in mental health research

Research showing the relationship between exposure to green space and health has yielded conflicting results, possibly due to the oversight of green space quality in quantitative studies. This systematic review, guided by the PRISMA framework (registered under Prospero ID CRD42023279720), focused on audit tools for green space quality in mental health research. From 4028 studies, 13 were reviewed, with 77 % linking better mental health outcomes to higher green space quality. Eight tools, especially Public Open Space and Dillen et al. tools demonstrated strong correlations with mental health. Certain green space qualities like grass, pathways, and water elements showed positive health associations. Future research should aim for standardized quality metrics and robust methodologies to support causal inferences and efficient assessments.

Dynamic greenspace exposure, individual mental health status and momentary stress level: A study using multiple greenspace measurements

Previous research on the relationship between greenspace exposure and mental health has largely taken a residence-based approach to exposure assessment, ignoring the dynamic nature of people's daily movements. Moreover, most studies evaluated greenspace from an overhead perspective, whereas an eye-level perspective could potentially offer a more comprehensive understanding of individuals' encounters with greenspaces. Based on our survey in two communities in Hong Kong (Sham Shui Po and Tin Shui Wai), we captured people's eye-level greenspace exposure based on their travel routes and visited places using GPS trajectories, streetscape images, and deep learning methods. We then compared the results with those obtained with an overhead greenness exposure measure (the normalized difference vegetation index [NDVI]). The results indicate that these two greenspace measurements are not associated with each other, implying that they encompass distinct facets of greenspace, which may have different effects on mental health. Further, we examined the associations between various greenspace exposure measures and mental health using GPS trajectories and ecological momentary assessment data. The results reveal a negative association between eye-level greenspace exposure and momentary stress, while no similar association was observed when using the top-down NDVI as an indicator of greenspace exposure. Moreover, compared to the total volume of greenspace exposure, the distance-weighted average of greenspace exposure based on dynamic mobility contexts has a stronger association with individual overall mental health. Lastly, the relationship between greenspace exposure and mental health varies between the two communities with different socio-economic attributes. The study indicates that policymakers should focus not only on residential neighborhoods and overhead greenspace but also consider the dynamic environments and socio-economic contexts that people are embedded in.

The relationship between nature exposure and depression among Chinese prisoners: a moderated mediation model

Aim

This study examined the association between self-reported nature exposure and depression among Chinese prisoners, as well as the mediating and moderating effects of meaning in life and callous-unemotional (CU) traits, respectively.

Background

Prisoners are more likely to experience depression than any other mental illness. Exposure to nature has been proposed as a highly cost-effective method of treating their depressive symptoms. However, the mechanism underlying the link between nature exposure and depression among prisoners needs further investigation, as the findings may provide new insights into how to address depression in incarcerated populations.

Method

Data were collected through a survey conducted in four prisons in southern China from April to May 2022. The participants were 574 prisoners who anonymously completed four questionnaires about nature exposure, meaning in life, depression, and CU traits.

Results

The results show that: (1) meaning in life significantly mediates the association between nature exposure and depression, and (2) CU traits moderate the connection between nature exposure and meaning in life.

Conclusion

The current study uncovered that prisoners who contact more with the natural environment have a higher meaning in life and lower depression, and individuals with higher CU traits can benefit more from nature exposure.

Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet

OBJECTIVE

Owing to the harsh environment in high-altitude areas, drivers experience significant driving stress. Compared with urban roads or expressways in low-altitude areas, the driving environment in high-altitude areas has distinct features, including mountainous environments and a higher proportion of trucks and buses. This study aims to investigate the feasibility of predicting stress levels through elements in the driving environment.

METHODS

Naturalistic driving tests were conducted on an expressway in Tibet. Driving stress was assessed using heart rate variability (HRV)-based indicators and classified using K-means clustering. A DeepLabv3 model was built to conduct semantic segmentation and extract environment elements from the driving scenarios recorded through a camera next to the driver's eyes. A decision tree and 4 other ensemble learning models based on decision trees were built to predict driving stress levels using the environment elements.

RESULTS

Fifty-six indicators were extracted from the driving environment. Results of the prediction models demonstrate that extreme gradient boosting has the best overall performance with the F1 score (harmonic mean of the precision and recall) and G-mean (geometric mean of sensitivity and specificity) reaching 0.855 and 0.890, respectively. Indicators based on the variation rate of trucks and buses have high feature importance and exhibit positive effects on driving stress. Indicators reflecting the proportion of mountain, road, and sky features negatively affect the expected levels of driving stress. Additionally, the mountain feature demonstrates multidimensional effects, because driving stress is positively affected by indicators of the variation rate for mountain elements.

CONCLUSIONS

This study validates the prediction of driving stress using environment elements in the driver's field of view and extends its application to high-altitude expressways with distinct environmental characteristics. This method provides a real-time, less intrusive, and safer method of driving stress assessment and prediction and also enhances the understanding of the environmental determinants of driving stress. The results hold promising applications, including the development of a driving state assessment and warning module as well as the identification of high-risk road sections and implementation of control measures.

"Urban-Satellite" estimates in the ABCD Study: Linking Neuroimaging and Mental Health to Satellite Imagery Measurements of Macro Environmental Factors

While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based "Urban-Satellite" (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subject's residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

The impact of outdoor blue spaces on the health of the elderly: A systematic review

Research on natural health has identified the potential benefit of outdoor blue spaces for human health and wellbeing. However, the existing evidence has relatively limited attention to the elderly. This study aims to review the available evidence on outdoor blue spaces and health outcomes among older individuals and identify knowledge gaps. In accordance with the PRISMA guidelines, specific keywords were used to search for articles published in English from inception to October 2023. Five databases (Scopus, PubMed, Web of Science, CINAHL, and PsycINFO) were searched, and 22 studies were identified in this review. We classified articles based on elderly health as general health (e.g., self-reported, perceived health and wellbeing), physical health (e.g., physical activity, physical function index), and mental health and wellbeing (e.g., depression). The findings indicated a positive correlation between outdoor blue space and the health of the elderly. In terms of the characteristics of exposure to outdoor blue spaces, direct contact (e.g., sensory-based) has not been well documented compared to indirect contact (e.g., distance, percentage, region-based). Although encouraging, the available body of evidence is limited and lacks consistency. Future research is needed to provide complementary evidence between outdoor blue spaces and elderly health.

Beneficial associations between outdoor visible greenness at the workplace and metabolic syndrome in Chinese adults

BACKGROUND

Greenness surrounding residential places has been found to significantly reduce the risk of diseases such as hypertension, obesity, and metabolic syndrome (MetS). However, it is unclear whether visible greenness exposure at the workplace has any impact on the risk of MetS.

METHODS

Visible greenness exposure was assessed using a Green View Index (GVI) based on street view images through a convolutional neural network model. We utilized logistic regression to examine the cross-sectional association between GVI and MetS as well as its components among 51,552 adults aged 18-60 in the city of Hangzhou, China, from January 2018 to December 2021. Stratified analyses were conducted by age and sex groups. Furthermore, a scenario analysis was conducted to investigate the risks of having MetS among adults in different GVI scenarios.

RESULTS

The mean age of the participants was 40.1, and 38.5% were women. We found a statistically significant association between GVI and having MetS. Compared to the lowest quartile of GVI, participants in the highest quartile of GVI had a 17% (95% CI: 11-23%) lower odds of having MetS. The protective association was stronger in the males, but we did not observe such differences in different age groups. Furthermore, we found inverse associations between GVI and the odds of hypertension, low high-density lipoprotein cholesterol, obesity, and high levels of FPG.

CONCLUSIONS

Higher exposure to outdoor visible greenness in the workplace environment might have a protective effect against MetS.

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

BACKGROUND

As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area.

METHODS

This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used.

RESULTS

A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data.

CONCLUSION

The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.

The benefits of nature exposure: The need for research that better informs implementation

Concern about humanity's detachment from nature has spawned a global push to increase the availability of green spaces within cities. One impetus for this movement is a growing collection of studies documenting an association between improved human well-being and exposure to nature. The challenge lies in translating this research into pragmatic recommendations for cities. The usefulness of the existing research portfolio is diminished by the limitations of prevailing research designs. For example, most nature exposure studies (>80%) are observational. The rare randomized manipulative experiments tend to be indoors or virtual and rely on nature exposures on the order of ten to fifteen minutes. "Nature" and "biodiversity" are commonly invoked together as benefiting human well-being despite little evidence that biodiversity has particular importance for human psychological and emotional health. The most glaring gap in nature exposure research is the neglect of differences among cultures and ethnic groups with respect to the nature they prefer. In the few cases where researchers looked for differences among groups, they often found heterogeneous responses. Finally, few studies have compared greening interventions to other possible efforts to improve urban life. Thus, the utopian city of the future might be resplendent with urban parks on every block, but it is not clear whether those parks should offer basketball and pickleball courts, or small woodlands with a cornucopia of birds. We advocate for the next generation of nature exposure research that better informs the envisioning of our future sustainable cities with enhanced and equitable access to nature.

Depression prediction based on LassoNet-RNN model: A longitudinal study

Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health.

Analysis of Heat Exposure During Pregnancy and Severe Maternal Morbidity

Importance

The rate of severe maternal morbidity (SMM) is continuously increasing in the US. Evidence regarding the associations of climate-related exposure, such as environmental heat, with SMM is lacking.

Objective

To examine associations between long- and short-term maternal heat exposure and SMM.

Design, Setting, and Participants

This retrospective population-based epidemiological cohort study took place at a large integrated health care organization, Kaiser Permanente Southern California, between January 1, 2008, and December 31, 2018. Data were analyzed from February to April 2023. Singleton pregnancies with data on SMM diagnosis status were included.

Exposures

Moderate, high, and extreme heat days, defined as daily maximum temperatures exceeding the 75th, 90th, and 95th percentiles of the time series data from May through September 2007 to 2018 in Southern California, respectively. Long-term exposures were measured by the proportions of different heat days during pregnancy and by trimester. Short-term exposures were represented by binary variables of heatwaves with 9 different definitions (combining percentile thresholds with 3 durations; ie, ≥2, ≥3, and ≥4 consecutive days) during the last gestational week.

Main Outcomes and Measures

The primary outcome was SMM during delivery hospitalization, measured by 20 subconditions excluding blood transfusion. Discrete-time logistic regression was used to estimate associations with long- and short-term heat exposure. Effect modification by maternal characteristics and green space exposure was examined using interaction terms.

Results

There were 3446 SMM cases (0.9%) among 403 602 pregnancies (mean [SD] age, 30.3 [5.7] years). Significant associations were observed with long-term heat exposure during pregnancy and during the third trimester. High exposure (≥80th percentile of the proportions) to extreme heat days during pregnancy and during the third trimester were associated with a 27% (95% CI, 17%-37%; P < .001) and 28% (95% CI, 17%-41%; P < .001) increase in risk of SMM, respectively. Elevated SMM risks were significantly associated with short-term heatwave exposure under all heatwave definitions. The magnitude of associations generally increased from the least severe (HWD1: daily maximum temperature >75th percentile lasting for ≥2 days; odds ratio [OR], 1.32; 95% CI, 1.17-1.48; P < .001) to the most severe heatwave exposure (HWD9: daily maximum temperature >95th percentile lasting for ≥4 days; OR, 2.39; 95% CI, 1.62-3.54; P < .001). Greater associations were observed among mothers with lower educational attainment (OR for high exposure to extreme heat days during pregnancy, 1.43; 95% CI, 1.26-1.63; P < .001) or whose pregnancies started in the cold season (November through April; OR, 1.37; 95% CI, 1.24-1.53; P < .001).

Conclusions and Relevance

In this retrospective cohort study, long- and short-term heat exposure during pregnancy was associated with higher risk of SMM. These results might have important implications for SMM prevention, particularly in a changing climate.

Spatial scale analysis for the relationships between the built environment and cardiovascular disease based on multi-source data

To examine what built environment characteristics improve the health outcomes of human beings is always a hot issue. While a growing literature has analyzed the link between the built environment and health, few studies have investigated this relationship across different spatial scales. In this study, eighteen variables were selected from multi-source data and reduced to eight built environment attributes using principal component analysis. These attributes included socioeconomic deprivation, urban density, street walkability, land-use diversity, blue-green space, transportation convenience, ageing, and street insecurity. Multiscale geographically weighted regression was then employed to clarify how these attributes relate to cardiovascular disease at different scales. The results indicated that: (1) multiscale geographically weighted regression showed a better fit of the association between the built environment and cardiovascular diseases than other models (e.g., ordinary least squares and geographically weighted regression), and is thus an effective approach for multiscale analysis of the built environment and health associations; (2) built environment variables related to cardiovascular diseases can be divided into global variables with large scales (e.g., socioeconomic deprivation, street walkability, land-use diversity, blue-green space, transportation convenience, and ageing) and local variables with small scales (e.g., urban density and street insecurity); and (3) at specific spatial scales, global variables had trivial spatial variation across the area, while local variables showed significant gradients. These findings provide greater insight into the association between the built environment and lifestyle-related diseases in densely populated cities, emphasizing the significance of hierarchical and place-specific policy formation in health interventions.

Effect Modifications of Overhead-View and Eye-Level Urban Greenery on Heat-Mortality Associations: Small-Area Analyses Using Case Time Series Design and Different Greenery Measurements

BACKGROUND

The protective effect of urban greenery from adverse heat impacts remains inconclusive. Existing inconsistent findings could be attributed to the different estimation techniques used.

OBJECTIVES

We investigated how effect modifications of urban greenery on heat-mortality associations vary when using different greenery measurements reflecting overhead-view and eye-level urban greenery.

METHODS

We collected meteorological and daily mortality data for 286 territory planning units between 2005 and 2018 in Hong Kong. Three greenery measurements were extracted for each unit: a) the normalized difference vegetation index (NDVI) from Landsat remote sensing images, b) the percentage of greenspace based on land use data, and c) eye-level street greenery from street view images via a deep learning technique. Time-series analyses were performed using the case time series design with a linear interaction between the temperature term and each of the three greenery measurements. Effect modifications were also estimated for different age groups, sex categories, and cause-specific diseases.

RESULTS

Higher mortality risks were associated with both moderate and extreme heat, with relative risks (RRs) of 1.022 (95% CI: 1.000, 1.044) and 1.045 (95% CI: 1.013, 1.079) at the 90th and 99th percentiles of temperatures relative to the minimum mortality temperature (MMT). Lower RRs were observed in greener areas whichever of the three greenery measurements was used, but the disparity of RRs between areas with low and high levels of urban greenery was more apparent when using eye-level street greenery as the index at high temperatures (99th percentile relative to MMT), with RRs for low and high levels of greenery, respectively, of 1.096 (95% CI: 1.035, 1.161) and 0.985 (95% CI: 0.920, 1.055) for NDVI (p = 0.0193 ), 1.068 (95% CI: 1.021, 1.117) and 0.990 (95% CI: 0.906, 1.081) for the percentage of greenspace (p = 0.1338 ), and 1.103 (95% CI: 1.034, 1.177) and 0.943 (95% CI: 0.841, 1.057) for eye-level street greenery (p = 0.0186 ). Health discrepancies remained for nonaccidental mortality and cardiorespiratory diseases and were more apparent for older adults (≥ 65 years of age) and females.

DISCUSSION

This study provides new evidence that eye-level street greenery shows stronger associations with reduced heat-mortality risks compared with overhead-view greenery based on NDVI and percentage of greenspace. The effect modification of urban greenery tends to be amplified as temperatures rise and are more apparent in older adults and females. Heat mitigation strategies and health interventions, in particular with regard to accessible and visible greenery, are needed for helping heat-sensitive subpopulation groups in coping with extreme heat. https://doi.org/10.1289/EHP12589.

Effect of built environment on BMI of older adults in regions of different socio-economic statuses

Background

Numerous studies have ignored the influence of underdeveloped urban surroundings on the physical health of China's ageing population. Lanzhou is a typical representative of a less developed city in China.

Methods

This study investigated the relationship between body mass index (BMI) and built environment amongst older adults in regions of different socio-economic statuses (SES) using data from medical examinations of older adults in Lanzhou, as well as calculating community built environment indicators for regions of different SES based on multiple linear regression models.

Results

Results showed that age and underlying disease were negatively associated with overall older adult BMI in the study buffer zone. Land use mix, number of parks and streetscape greenery were positively associated with older adult BMI. Street design and distance to bus stops were negatively connected in low SES regions, but population density and street design were negatively correlated in high SES areas.

Conclusion

These findings indicate that the built environment of SES regions has varying impacts on the BMI of older persons and that planners may establish strategies to lower the incidence of obesity amongst older adults in different SES locations.

Satisfaction with urban trees associates with tree canopy cover and tree visibility around the home

Many world cities want to expand the number of urban trees. How this expansion occurs should consider what people expect from trees based on how they experience and perceive these trees. Therefore, we need a better understanding of how people perceptually respond to urban tree abundance. This research examined whether people's satisfaction with urban trees and satisfaction with the management of those trees were related to objective measures of greenery such as the Normalized Difference Vegetation Index (NDVI), percent tree canopy cover, and the Viewshed Greenness Visibility Index (VGVI) for trees. We used a demographically and geographically representative survey of 223 residents in Toronto, Canada, and calculated NDVI, canopy cover, and VGVI at three neighbourhood sizes. We analysed the data using generalized linear regression. We found that canopy cover and VGVI had a positive association with satisfaction with urban trees. The associations were comparatively stronger at larger neighbourhood scales than at smaller scales. There were no statistically significant associations with NDVI or satisfaction with the management of urban trees.

Association between urban green space and postpartum depression, and the role of physical activity: a retrospective cohort study in Southern California

Background

Little research exists regarding the relationships between green space and postpartum depression (PPD). We aimed to investigate the relationships between PPD and green space exposure, and the mediating role of physical activity (PA).

Methods

Clinical data were obtained from Kaiser Permanente Southern California electronic health records in 2008-2018. PPD ascertainment was based on both diagnostic codes and prescription medications. Maternal residential green space exposures were assessed using street view-based measures and vegetation types (i.e., street tree, low-lying vegetation, and grass), satellite-based measures [i.e., Normalized Difference Vegetation Index (NDVI), land-cover green space, and tree canopy cover], and proximity to the nearest park. Multilevel logistic regression was applied to estimate the association between green space and PPD. A causal mediation analysis was performed to estimate the proportion mediated by PA during pregnancy in the total effects of green space on PPD.

Findings

In total, we included 415,020 participants (30.2 ± 5.8 years) with 43,399 (10.5%) PPD cases. Hispanic mothers accounted for about half of the total population. A reduced risk for PPD was associated with total green space exposure based on street-view measure [500 m buffer, adjusted odds ratio (OR) per interquartile range: 0.98, 95% CI: 0.97-0.99], but not NDVI, land-cover greenness, or proximity to a park. Compared to other types of green space, tree coverage showed stronger protective effects (500 m buffer, OR = 0.98, 95% CI: 0.97-0.99). The proportions of mediation effects attributable to PA during pregnancy ranged from 2.7% to 7.2% across green space indicators.

Interpretation

Street view-based green space and tree coverage were associated with a decreased risk of PPD. The observed association was primarily due to increased tree coverage, rather than low-lying vegetation or grass. Increased PA was a plausible pathway linking green space to lower risk for PPD.

Funding

National Institute of Environmental Health Sciences (NIEHS; R01ES030353).

Does the quality of street greenspace matter? Examining the associations between multiple greenspace exposures and chronic health conditions of urban residents in a rapidly urbanising Chinese city

BACKGROUND

Numerous studies have demonstrated that greenspace(GS) exposure is associated with health improvements in individuals with hypertension and diabetes. However, studies examining the associations between multiple GS exposures and chronic health conditions in developing countries are limited.

METHODS

Geospatial data and spatial analysis were employed to objectively measure the total neighbourhood vegetative cover (mean value of normalised difference vegetation index [NDVI] within specific buffer zone) and proximity to park-based GS (network distance from home to the entrance of park-based GS). Street view imagery and machine learning techniques were used to measure the subjective perceptions of street GS quality. A multiple linear regression model was applied to examine the associations between multiple GS exposures and the prevalence of hypertension and diabetes in neighbourhoods located in Qingdao, China.

RESULTS

The model explained 29.8% and 28.2% of the prevalence of hypertension and diabetes, respectively. The results suggested that: 1) the total vegetative cover of the neighbourhood was inversely correlated with the prevalence of hypertension (β = -0.272, p = 0.013, 95% confidence interval (CI): [-1.332, -0.162]) and diabetes (β = -0.230, p = 0.037, 95% CI: [-0.720, -0.008]). 2) The street GS quality was negatively correlated with the prevalence of hypertension (β = -0.303, p = 0.007, 95% CI: [-2.981, -0.491]) and diabetes (β = -0.309, p = 0.006, 95% CI: [-1.839, -0.314]). 3) Proximity to park-based GS and the prevalence of hypertension and diabetes mellitus were not significantly correlated.

CONCLUSIONS

This study used subjective and objective methods to comprehensively assess the greenspace exposure from overhead to eye level, from quantity, proximity to quality. The results demonstrated the beneficial relationships between street GS quality, total vegetative cover, and chronic health in a rapidly urbanising Chinese city. Furthermore. the effect of street GS quality was more pronounced in potentially mitigating chronic health problems, and improving the quality of street GS might be an efficient and effective intervention pathway for addressing chronic health issues in densely populated cities.

Associations between exposure to blue spaces and natural and cause-specific mortality in Greece: An ecological study

BACKGROUND

A growing body of evidence suggests that exposure to natural environments, such as green space, may have a beneficial role in health. However, there is limited evidence regarding the effects of exposure to blue spaces and mortality. We investigated the association of exposure to blue spaces with natural and cause-specific mortality in Greece using an ecological study design METHODS: Mortality and socioeconomic data were obtained from 1,035 municipal units (MUs) from the 2011 census data. To define exposure to "blue" we used a rate of the land cover categories related to blue space from the COoRdination and INformation on the Environmental (CORINE) 2012 map per 10,000 persons in the municipal unit. We further assessed the exposure to blue space in the MUs that are located in the coastline of Greece using the distance to the coast as a proxy for proximity to blue space. the Annual PM_2.5, NO₂, BC and O₃ concentrations for 2010 were predicted by land use regression models while the normalized difference vegetation index was used to assess greenness. We applied single and two exposure Poisson regression models accounting for spatial autocorrelation and adjusting for unemployment and lung cancer mortality rates, percentages of the population aged 25-64 with upper secondary or tertiary education attainment and of those born in Greece, and urbanicity. The analysis was conducted for the whole country and separately by varying geographical definitions.

RESULTS

An interquartile range (IQR) increase of blue space per 10,000 persons was associated with decreased risk in natural mortality (Relative Risk (RR): 0.98 (95% confidence interval (CI): 0.98, 0.99), as well as in mortality due to cardiovascular causes, respiratory causes and diseases of the nervous system 0.98 (95% CI: 0.97, 0.99); 0.97 (95% CI: 0.95, 0.99); 0.94 (95% CI: 0.88, 1.00) respectively). We estimated protective associations for ischemic heart disease (IHD) mortality (RR = 0.98, 95% CI: 0.97, 1.00 per IQR); COPD mortality (RR = 0.97, 95% CI: 0.93, 1.00 per IQR) and mortality from cerebrovascular disease (RR = 0.97 (95% CI: 0.96, 0.99 per IQR). We estimated protective associations for the distance from the coast and mortality from the diseases of the nervous system (RR = 0.75, 95% CI: 0.61, 0.92, ≤1 km from the coast versus >1 km). Our results were stronger for inhabitants of the islands, the coastline and in the rural areas of Greece while the estimates were robust to co-exposure adjustment.

CONCLUSIONS

We estimated statistically significant protective effects of exposure to blue space on mortality from natural, cardiovascular and respiratory causes, diseases of the nervous system, cerebrovascular and ischemic heart disease for in Greece with higher estimates in the coastline and the islands. Further research is needed to elaborate our findings.

Associations of residential greenness with unhealthy consumption behaviors: Evidence from high-density Hong Kong using street-view and conventional exposure metrics

AIM

Residential greenness was theoretically associated with health-related consumption behaviors concerning the socio-ecological model and restoration environment theory, but empirical studies were limited, especially in high-density cities. We examined the associations of residential greenness with unhealthy consumption behaviors (infrequent breakfast consumption, infrequent fruit consumption, infrequent vegetable consumption, alcohol drinking, binge drinking, cigarette smoking, moderate-to-heavy smoking, and heavy smoking) using street-view and conventional greenness metrics in high-density Hong Kong.

METHODS

This cross-sectional study employed survey data from 1,977 adults and residence-based objective environmental data in Hong Kong. Street-view greenness (SVG) was extracted from Google Street View images using an object-based image classification algorithm. Two conventional greenness metrics were used, including normalized difference vegetation index (NDVI) derived from Landsat 8 remote-sensing images and park density derived from a geographic information system database. In the main analyses, logistic regression analyses together with interaction and stratified models were performed with environmental metrics measured within a 1000-m buffer of residence.

RESULTS

A standard deviation higher SVG and NDVI were significantly associated with fewer odds of infrequent breakfast consumption (OR = 0.81, 95% CI 0.71-0.94 for SVG; OR = 0.83, 95% CI 0.73-0.95 for NDVI), infrequent fruit consumption (OR = 0.85, 95% CI 0.77-0.94 for SVG; OR = 0.85, 95% CI 0.77-0.94 for NDVI), and infrequent vegetable consumption (OR = 0.78, 95% CI 0.66-0.92 for SVG; OR = 0.81, 95% CI 0.69-0.94 for NDVI). The higher SVG was significantly associated with less binge drinking and the higher SVG at a 400-m buffer and a 600-m buffer were significantly associated with less heavy smoking. Park density was not significantly associated with any unhealthy consumption behaviors. Some of the above significant associations were moderated by moderate physical activity, mental and physical health, age, monthly income, and marital status.

CONCLUSIONS

This study highlights the potential beneficial impact of residential greenness, especially in terms of street greenery, on healthier eating habits, less binge drinking, and less heavy smoking.

The effects of audio-visual perceptual characteristics on environmental health of pedestrian streets with traffic noise: A case study in Dalian, China

The COVID-19 pandemic has affected city dwellers’ physical and mental health and has raised concerns about the health of urban public spaces. This field investigation research in Dalian, China, examined the perceived audio-visual environment characteristics of urban pedestrian streets with traffic noise and their influences on the environmental health of the pedestrian streets. Five indicators reflecting psychological responses to environmental characteristics (willingness to walk, relaxation, safety, beauty, and comprehensive comfort) were used to measure environmental health of pedestrian streets with traffic noise. The results showed that safety was rated the highest, and willingness to walk was evaluated as the lowest among health evaluation indicators. The imageability and openness of the streetscape were associated with each health evaluation indicator. In contrast, the rhythm and continuity of the street buildings had a greater effect on willingness to walk than the other health indicators. There were negative correlations between LAeq for traffic noise and health evaluations. Positive health evaluations were observed when LAeq was less than 55 dBA. In contrast, soundscape indicators showed positive correlations with health evaluations, and acoustic comfort and noise annoyance, rather than sound preference and subjective loudness were associated with each health evaluation indicator. In terms of the combined audio-visual factors, acoustic comfort, the quantity of greening, annoyance, sky visibility, spatial scale, and building distance were examined as the determining factors affecting health evaluations, and 55.40% of the variance in health evaluations was explained by the soundscape and streetscape indicators. The findings provide references for better understanding the relationships between healthy experience and audio-visual perceptions. Moreover, they enable environmental health quality optimisation of pedestrian spaces considering audio-visual indicators and approaches in the post-epidemic era.

Sea swimming and snorkeling in tropical coastal blue spaces and mental well-being: Findings from Indonesian island communities during the COVID-19 pandemic

The COVID-19 pandemic has considerable mental health impacts. Immersive nature-based interventions, such as swimming or snorkeling, may help mitigate the global mental health crisis caused by the pandemic. To investigate this, we collected cross-sectional data from residents of coastal villages (n = 308) in Kepulauan Selayar, Indonesia. Analysis of Covariance (ANCOVA) was used with mental well-being as the outcome variable, operationalized as the Mental Component Summary (MCS) scores from the SF-12 (12-item Short Form Health Survey). After adjusting for covariates, the activity of sea swimming or snorkeling was found to be significantly associated with better mental well-being (η² = 0.036; p < 0.01). Predictive margins analysis revealed that those who engaged in sea swimming or snorkeling for one to three days a week gained a 2.7 increase in their MCS scores, compared to those who did not. A non-linear dose-response relationship was detected: for those swimming or snorkeling more than three days per week, there was only an increase of 1.7 MCS score compared to the 0-day. Overall this study contributes to the expanding of evidence base, showing that interactions with blue spaces can be beneficial for mental health, especially in a potentially stressful time such as the current pandemic.

Management implications

The positive association between the activity of swimming or snorkeling in open seas and the mental well-being of rural coastal communities in Indonesia during the COVID-19 pandemic indicates that access to coastal blue spaces is important in a time of uncertainties and high stress. Ensuring that local communities have continuous access to these spaces is the key challenge for all relevant stakeholders, particularly in light of the growing privatization of the local coastal environment for the sake of tourism. However, considering the importance that these blue spaces hold for the mental well-being of local communities, intensive dialogue amongst these stakeholders must be pursued to ensure that the development of the area does not jeopardize the collective well-being of the people already living there.

The effect of greenness on allergic rhinitis outcomes in children and adolescents: A systematic review and meta-analysis

BACKGROUND

The relationship between greenness and health emerges as new public health concern. More published studies from multiple areas have explored the relationship between greenness and allergic rhinitis (AR) in children and adolescents. This study aims to determine the association between greenness and allergic rhinitis by systematic review and meta-analysis, in order to provide a more comprehensive assessment of the impact of greenness on AR in children and adolescents.

METHODS

The relative literature was systematically searched in PubMed, Embase, and Web of science lastly on September 25, 2022. Terms related to greenness and allergic rhinitis were used for searching. Summary effect estimates of greenness on AR in children and adolescents were calculated for per 10 % increase of greenness exposure with different buffer sizes by random-effects model.

RESULTS

A total of 579 studies were screened, and fourteen studies from Europe, Asia and North America were finally included. Most greenness exposure were measured by normalized difference vegetation index (NDVI). Enhanced vegetation index, outdoor-green environmental score and existed to measuring different greenness types. Greenness surrounding residences and schools were assessed. The overall effect of greenness on primary outcome was 1.00 (95%CI = 0.99-1.00). Most effect estimates of greenness were included in the NDVI-500 m group, and the pooled OR was 0.99 (95%CI = 0.97-1.01). No significant pooled estimates were found in analyses with study locations.

CONCLUSION

This study indicates no significant association between greenness exposure and AR in children and adolescents. Various exposure measures and conversion of data may affect the results of this meta-analysis. More precise assessment of personal greenness exposure in well-designed prospective studies are vital for drawing a definite association in future. Furthermore, greenness exposure surrounding schools should be paid considerable attention for its effect on AR in school-aged children and adolescents.

Impact of the Built Environment on Residents' Health: Evidence from the China Labor Dynamics Survey in 2016

In the process of China's rapid urbanization, the health level of residents has been improved to a great extent. However, with the expansion of urban scale and spatial restructuring, a series of urban environmental problems have posed new challenges to public health. However, the impact of the built environment on residents' health is controversial, and the applicability of the conclusions based on western urban sprawl in China is not clear enough. In addition, the exploration of the impact path of the built environment on health is still not comprehensive and in-depth. Based on the China Labor Dynamics Survey (CLDS) in 2016 and relevant statistical yearbook data, this study explored the impact of the built environment at community and urban scale on residents' health and its age heterogeneity and further explored the mediating role of physical exercise, neighborhood support, and community safety. According to the research, the urban and community-built environment has significant impacts on residents' health, and the impact is significantly different at different scales. In addition, there is a significant difference in the impact of built environment factors on residents' health among populations with different life cycles. From the perspective of the impact path, greening coverage can improve residents' self-rated health by enhancing the perceived safety of living in the community. In contrast, the high community population density will not only weaken the degree of neighborhood support but also reduce the perception level of community residential safety, thus damaging residents' health. In short, from the perspective of environmental intervention, the previously mentioned results put forward possible suggestions on strengthening the construction of a healthy living environment so as to maximize the health effectiveness of cities and communities.

Using geospatial social media data for infectious disease studies: a systematic review

Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning

BACKGROUND

Natural environment might encourage physical exercise, hence enhancing human health and wellbeing. Social media offers an extensive repository of spatiotemporal data, containing details on the feelings and behaviors of individuals. However, investigations on physical activity and public sentiment in the natural environment of the downtown neighborhood are lacking in the existing literature.

METHODS

To extract environmental and behavioral information from social media data and other multi-source data, natural language processing, semantic segmentation, instance segmentation, and fully convolutional neural networks are employed. The research examines how neighborhood blue-green spaces and other health-promoting facilities affect physical activity and public sentiment.

RESULTS

The results reveal that blue space visibility, activity facilities, street furniture, and safety all have a favorable influence on physical activity with a social gradient. Amenities, perceived street safety and beauty positively correlated to public sentiment. The findings from social media about the environment and physical activity are consistent with traditional surveys from the same time period with a 0.588 kappa value.

CONCLUSION

According to our findings, social media data might be utilized to learn more about how urban environments influence people's physical activity patterns. Also, the health-promoting effects of blue space require more investigation.

The impact of residential greenness on psychological distress among Hurricane Katrina survivors

Residential greenness may support mental health among disaster-affected populations; however, changes in residential greenness may disrupt survivors' sense of place. We obtained one pre- and three post-disaster psychological distress scores (Kessler [K]-6) from a cohort (n = 229) of low-income mothers who survived Hurricane Katrina in New Orleans, Louisiana, USA. Greenness was assessed using average growing season Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in the 300 m around participants' homes at each time point. We used multivariable logistic regressions to evaluate two hypotheses: 1) that cross-sectional greenness (above vs. below median) was associated with reduced psychological distress (K6≥5); and 2) that changes in residential greenness were associated with adverse mental health. When using EVI, we found that a change in level of greenness (i.e., from high to low [high-low], or from low to high [low-high] greenness, comparing pre- and post-Katrina neighborhoods) was associated with increased odds of distress at the first post-storm survey, compared to moving between or staying within low greenness neighborhoods (low-high odds ratio [OR] = 3.48; 95% confidence interval [CI] = 1.40, 8.62 and high-low OR = 2.60; 95% CI: 1.05, 6.42). Results for NDVI were not statistically significant. More research is needed to characterize how residential greenness may impact the health of disaster survivors, and how these associations may change over time.

Do Greener Urban Streets Provide Better Emotional Experiences? An Experimental Study on Chinese Tourists

Compared to the usual environment, the potential momentary emotional benefits of exposure to street-level urban green spaces (UGS) in the unusual environment have not received much academic attention. This study applies an online randomized control trial (RCT) with 299 potential tourists who have never visited Xi'an and proposes a regression model with mixed effects to scrutinize the momentary emotional effects of three scales (i.e., small, medium and large) and street types (i.e., traffic lanes, commercial pedestrian streets and culture and leisure walking streets). The results identify the possibility of causality between street-level UGS and tourists' momentary emotional experiences and indicate that tourists have better momentary emotional experiences when urban streets are intervened with large-scale green vegetation. The positive magnitude of the effect varies in all three types of streets and scales of intervention, while the walking streets with typical cultural attractions, have a larger impact relative to those with daily commute elements. These research results can provide guidance for UGS planning and the green design of walking streets in tourism.

Nonlinear Effects of the Neighborhood Environments on Residents' Mental Health

In the context of rapid urbanization and the "Healthy China" strategy, neighborhood environments play an important role in improving mental health among urban residents. While an increasing number of studies have explored the linear relationships between neighborhood environments and mental health, much remains to be revealed about the nonlinear health effects of neighborhood environments, the thresholds of various environmental factors, and the optimal environmental exposure levels for residents. To fill these gaps, this paper collected survey data from 1003 adult residents in Guangzhou, China, and measured the built and social environments within the neighborhoods. The random forest model was then employed to examine the nonlinear effects of neighborhood environments on mental health, evaluate the importance of each environmental variable, as well as identify the thresholds and optimal levels of various environmental factors. The results indicated that there are differences in the importance of diverse neighborhood environmental factors affecting mental health, and the more critical environmental factors included greenness, neighborhood communication, and fitness facility density. The nonlinear effects were shown to be universal and varied among neighborhood environmental factors, which could be classified into two categories: (i) higher exposure levels of some environmental factors (e.g., greenness, neighborhood communication, and neighborhood safety) were associated with better mental health; (ii) appropriate exposure levels of some environmental factors (e.g., medical, fitness, and entertainment facilities, and public transport stations) had positive effects on mental health, whereas a much higher or lower exposure level exerted a negative impact. Additionally, this study identified the exact thresholds and optimal exposure levels of neighborhood environmental factors, such as the threshold (22.00%) and optimal exposure level (>22.00%) of greenness and the threshold (3.80 number/km²) and optimal exposure level (3.80 number/km²) of fitness facility density.

Impacts of Urban Blue-Green Space on Residents' Health: A Bibliometric Review

Urban blue-green space (UBGS), as an important component of the urban environment, is found to closely relate to human health. An extensive understanding of the effects of UBGS on human health is necessary for urban planning and intervention schemes towards healthy city development. However, a comprehensive review and discussion of relevant studies using bibliometric methods is still lacking. This paper adopted the bibliometric method and knowledge graph visualization technology to analyze the research on the impact of UBGS on residents' health, including the number of published papers, international influence, and network characteristics of keyword hotspots. The key findings include: (1) The number of articles published between 2001 and 2021 shows an increasing trend. Among the articles collected from WoS and CNKI, 38.74% and 32.65% of the articles focus on physical health, 38.32% and 30.61% on mental health, and 17.06% and 30.61% on public health, respectively. (2) From the analysis of international partnerships, countries with high levels of economic development and urbanization have closer cooperation than other countries. (3) UBGS has proven positive effects on residents' physical, mental, and public health. However, the mediating effects of UBGS on health and the differences in the health effects of UBGS on different ages and social classes are less studied. Therefore, this study proposes several future research directions. First, the mediating effect of UBGS on health impacts should be further examined. Furthermore, the interactive effects of residents' behaviors and the UBGS environment should be emphasized. Moreover, multidisciplinary integration should be strengthened. The coupling mechanism between human behavior and the environment should also be studied in depth with the help of social perception big data, wearable devices, and human-computer interactive simulation. Finally, this study calls for developing health risk monitoring and early warning systems, and integrating health impact assessment into urban planning, so as to improve residents' health and urban sustainability.

The Effects of Public Open Space on Older People's Well-Being: From Neighborhood Social Cohesion to Place Dependence

This quantitative study examines the effects of Public Open Space (POS) on older people's well-being and examines the roles of neighborhood social cohesion (NSC) and place dependence (PD) as series buffers. A questionnaire survey of 501 people aged 65 and over was conducted in various communities of Hong Kong. Structural equation modelling (SEM) was used to analyze the pathways connecting POS and well-being. A multigroup analysis examined differences in the POS-well-being associations between the young-old (aged 65 to 75, n = 166) and old-old group (aged 76 to 95, n = 166). Results show that the association between POS and emotional well-being was stronger than social and psychological well-being. POS promotes three facets of well-being through developing NSC and, subsequently, PD. Multigroup analysis results suggest that the pathway from POS to emotional well-being via NSC is stronger for the old-old group; POS is more important for psychological well-being for the young-old group. This study highlights that the quality of POS, including attractive natural elements, various amenities, and sufficient space for social interactions, is essential for making relationship-rich and health-promotive urban environments.

Association of NO2 with daily hospital admissions for mental disorders: Investigation of the modification effects of green spaces and long-term NO2 exposure

Air pollution is a risk factor for increased hospital admissions due to mental disorders, while green spaces have been linked with better mental health. We linked daily hospital admission records from Wuhan's 74 municipal hospitals from 2017 to 2019 with modeled annual average NO₂ concentrations and added data on the residential surrounding green spaces with 250 m and 500 m buffers based on the normalized difference vegetation index (NDVI) using a land use regression model (LUR). The conditional logistic regression model was used to estimate the acute effect of short-term NO₂ exposure, and stratification analyses were applied to explore the modification effect of long-term NO₂ exposure and green spaces by estimating the odds ratios in the single- and dual-environmental factor groups. A total of 42,705 hospital admissions for mental disorders were identified. Short-term exposure to NO₂ was associated with an increased risk of hospital admission for mental disorders. A 10 μg/m³ increase in NO₂ (lag01 day) was associated with an increase in hospital admissions of 2.86% (95% CI, 2.05-3.68) for the total mental disorders. Compared with patients in the "low-NDVI/low-NO₂" group (ER = 2.27%, 95% CI, 0.27-4.31), patients in the "high-NDVI/low-NO₂" group (ER = 1.93%, -0.10-3.99) showed a lower and insignificant increase in hospitalizations for the total mental disorders, while greenness had a slight moderating effect in the high-level long-term NO₂ exposure areas. This study suggested that green spaces may moderate the acute effect of NO₂ exposure for mental disorder hospitalizations, especially in low-level long-term NO₂ exposure areas.

Restorative Effects from Green Exposure: A Systematic Review and Meta-Analysis of Randomized Control Trials

Despite growing research on green space and health benefits, the body of evidence remains heterogeneous and unclear. A systematic review and meta-analysis of studies of randomized controlled trials (RCTs) with high evidence levels are deemed timely. We searched Scopus, PubMed, Embase, and Web of Science for the literature up to January 2022 and assessed bias using the Cochrane Risk of Bias tool 2.0. We calculated joint impact estimates for each green space exposure assessment technique using random and fixed effects models. Compared to non-green space situations, green space exposure was related to decreased negative feelings, such as fatigue -0.84 (95% CI: -1.15 to -0.54), and increased levels of pleasant emotions, such as vitality 0.85 (95% CI: 0.52 to 1.18). It also lowered physiological indicators, including heart rate levels, by 0.60 (95% CI: -0.90 to -0.31). Effect sizes were large and statistically significant, and the overall quality of the evidence was good. Existing RCTs on greenspace exposure pay insufficient attention to older and adolescent populations, different ethnic groups, different regions, and doses of greenspace exposure interventions. More research is needed to understand how and how much green space investment has the most restorative benefits and guide urban green space planning and renewal.

Integrated analysis of doubly disadvantaged neighborhoods by considering both green space and blue space accessibility and COVID-19 infection risk

The ongoing COVID-19 pandemic has taken a heavy toll on the physical and mental health of the public. Nevertheless, the presence of green and blue spaces has been shown to be able to encourage physical activities and alleviate the mental distress caused by COVID-19. However, just as the impact of COVID-19 varies by geographical region and area, the distribution of green and blue spaces is also different across different neighborhoods and areas. By using Hong Kong as the study area, we determine the local neighborhoods that suffer from both high COVID-19 infection risk as well as low green and blue space accessibility. The results show that some of the poorest neighborhoods in the territory such as Sham Shui Po, Kwun Tong and Wong Tai Sin are also among the most doubly disadvantaged in terms of COVID-19 infection risk as well as green and blue space accessibility.

The Effect of Psychological Burden on Dyslipidemia Moderated by Greenness: A Nationwide Study from China

Globally, dyslipidemia is now become a leading risk factor for many adverse health outcomes, especially in the middle-aged and elderly. Recent evidence suggests that exposure to greenness and the relief of a psychological burden may decrease the prevalence of dyslipidemia. The objective of our study was to examine whether a green space can moderate the association between mental health status and dyslipidemia. Our study selected the datasets of depression symptoms, dyslipidemia from the China Health and Retirement Longitudinal Study (CHARLS), and the satellite-based normalized difference vegetation index (NDVI) from the 30 m annual maximum NDVI dataset in China in 2018. Ultimately, a total of 10,022 middle-aged and elderly Chinese were involved in our study. Multilevel logistic regressions were performed to examine the association between symptoms of depression and dyslipidemia, as well as the moderate effect of greenness exposure on the association. Our research suggested that adults diagnosed with depression symptoms were more likely to suffer from dyslipidemia. In addition, the NDVI was shown to moderate the effect of depression on dyslipidemia significantly, though the effect was attenuated as depression increased. Regarding the moderate effect of the NDVI on the above association across age, gender, and residence, the findings presented that females, the elderly, and respondents living in urban areas were at a greater risk of having dyslipidemia, although the protective effect of the NDVI was considered. Likewise, the moderate effect of the NDVI gradually decreased as the level of depression increased in different groups. The current study conducted in China provides insights into the association between mental health, green space, and dyslipidemia. Hence, improving mental health and green spaces can be potential targets for medical interventions to decrease the prevalence of dyslipidemia.

GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative

Natural environments, such as parks, woodlands and lakes, have positive impacts on health and wellbeing. Urban Green and Blue Spaces (UGBS), and the activities that take place in them, can significantly influence the health outcomes of all communities, and reduce health inequalities. Improving access and quality of UGBS needs understanding of the range of systems (e.g. planning, transport, environment, community) in which UGBS are located. UGBS offers an ideal exemplar for testing systems innovations as it reflects place-based and whole society processes , with potential to reduce non-communicable disease (NCD) risk and associated social inequalities in health. UGBS can impact multiple behavioural and environmental aetiological pathways. However, the systems which desire, design, develop, and deliver UGBS are fragmented and siloed, with ineffective mechanisms for data generation, knowledge exchange and mobilisation. Further, UGBS need to be co-designed with and by those whose health could benefit most from them, so they are appropriate, accessible, valued and used well. This paper describes a major new prevention research programme and partnership, GroundsWell, which aims to transform UGBS-related systems by improving how we plan, design, evaluate and manage UGBS so that it benefits all communities, especially those who are in poorest health. We use a broad definition of health to include physical, mental, social wellbeing and quality of life. Our objectives are to transform systems so that UGBS are planned, developed, implemented, maintained and evaluated with our communities and data systems to enhance health and reduce inequalities. GroundsWell will use interdisciplinary, problem-solving approaches to accelerate and optimise community collaborations among citizens, users, implementers, policymakers and researchers to impact research, policy, practice and active citizenship. GroundsWell will be shaped and developed in three pioneer cities (Belfast, Edinburgh, Liverpool) and their regional contexts, with embedded translational mechanisms to ensure that outputs and impact have UK-wide and international application.

Assessing myocardial infarction severity from the urban environment perspective in Wuhan, China

Health inequalities are globally widespread due to the regional socioeconomic inequalities. Myocardial infarction (MI) is a leading health problem causing deaths worldwide. Yet medical services for it are often inequitably distributed by region. Moreover, studies concerning MI's potential spatial risk factors generally suffer from difficulties in focusing on too few factors, inappropriate models, and coarse spatial grain of data. To address these issues, this paper integrates registered 1098 MI cases and urban multi-source spatio-temporal big data, and spatially analyses the risk factors for MI severity by applying an advanced interpretable model, the random forest algorithm (RFA)-based SHapley Additive exPlanations (SHAP) model. In addition, a community-scale model between spatio-temporal risk factors and MI cases is constructed to predict the MI severity of all communities in Wuhan, China. The results suggest that those risk factors (i.e., age of patients, medical quality, temperature changes, air pollution and urban habitat) affect the MI severity at the community scale. We found that Wuhan residents in the downtown area are at risk for high MI severity, and the surrounding suburb areas show a donut-shape pattern of risk for medium-to-high MI severity. These patterns draw our attention to the impact of spatial environmental risk factors on MI severity. Thus, this paper provides three recommendations for urban planning to reduce the risk and mortality from severe MI in the aspect of policy implication.

Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F1-measure reaches 0.67.

Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.

Green Space for Mental Health in the COVID-19 Era: A Pathway Analysis in Residential Green Space Users

Residential green space is among the most accessible types of urban green spaces and may help maintain mental health during the COVID-19 pandemic. However, it is insufficiently understood how residents use residential green space for exercise during the epidemic. The pathways between residential green space and mental health also merit further exploration. Therefore, we conducted an online study among Chinese residents in December 2021 to capture data on engagement with urban green space for green exercise, the frequency of green exercise, perceived pollution in green space, perceptions of residential green space, social cohesion, depression, and anxiety. Among the 1208 respondents who engaged in green exercise last month, 967 (80%) reported that green exercise primarily occurred in residential neighborhoods. The rest (20%) reported that green exercise occurred in more distant urban green spaces. The most common reasons that respondents sought green exercise in urban green spaces were better air and environmental qualities. Structural equation modeling (SEM) was then employed to explore the pathways between the perceived greenness of residential neighborhoods and mental health among respondents who used residential green space for exercise. The final model suggested that residential green space was negatively associated with anxiety (β = −0.30, p = 0.001) and depression (β = −0.33, p < 0.001), mainly through indirect pathways. Perceived pollution and social cohesion were the two mediators that contributed to most of the indirect effects. Perceived pollution was also indirectly associated with green exercise through less social cohesion (β = −0.04, p = 0.010). These findings suggest a potential framework to understand the mental health benefits of residential green space and its accompanying pathways during the COVID-19 era.

Urban greenspace and mental health in Chinese older adults: Associations across different greenspace measures and mediating effects of environmental perceptions

This study aimed to contrast the associations of street view-, land use- and satellite-derived greenspace measures with older adults' mental health and to examine the mediating effects of neighborhood environmental perceptions (i.e., noise, aesthetics and satisfaction with recreational opportunities) to explain potential heterogeneity in the associations. Data of 879 respondents aged 60 or older in Dalian, China were used, and multilevel regression models were conducted in Stata. Results indicated that the Normalized Difference Vegetation Index (NDVI), vegetation coverage, park coverage and streetscape grasses were positively correlated with older adults' mental health. The associations of exposure metrics measured by overhead view were stronger than those measured by the street view. Streetscape grasses had a stronger association with older adults' mental health than streetscape trees. Noise, aesthetics and satisfaction with recreational opportunities mediated these associations, but the strength of the mediating effects differed across the greenspace measures. Our findings confirm the necessity of multi-measures assessment for greenspace to examine associations with older adults' mental health in Chinese settings and can contribute to the realization of health benefits of urban greenspace.